<h3>Experiment with 'Maus' aus input device
</h3><table border='1'>
<tr style='background-color:#AEAEAE'>
<td>
Trial
</td>
<td>
D
</td>
<td>
W
</td>
<td>
ID
</td>
<td>
Hits
</td>
<td>
Missed
</td>
<td>
MT
</td>
</tr>
<tr>
<td>
1
</td>
<td>
700
</td>
<td>
50
</td>
<td>
4.807354922057604
</td>
<td>
9
</td>
<td>
0
</td>
<td>
1.1111111111111112
</td>
</tr>
<tr>
<td>
2
</td>
<td>
30
</td>
<td>
10
</td>
<td>
2.584962500721156
</td>
<td>
11
</td>
<td>
1
</td>
<td>
0.9090909090909092
</td>
</tr>
<tr>
<td>
3
</td>
<td>
280
</td>
<td>
25
</td>
<td>
4.459431618637297
</td>
<td>
8
</td>
<td>
1
</td>
<td>
1.25
</td>
</tr>
<tr>
<td>
4
</td>
<td>
20
</td>
<td>
20
</td>
<td>
1.0
</td>
<td>
13
</td>
<td>
0
</td>
<td>
0.7692307692307693
</td>
</tr>
<tr>
<td>
5
</td>
<td>
500
</td>
<td>
40
</td>
<td>
4.643856189774724
</td>
<td>
9
</td>
<td>
0
</td>
<td>
1.1111111111111112
</td>
</tr>
<tr>
<td>
6
</td>
<td>
300
</td>
<td>
100
</td>
<td>
2.584962500721156
</td>
<td>
11
</td>
<td>
1
</td>
<td>
0.9090909090909092
</td>
</tr>
<tr>
<td>
7
</td>
<td>
160
</td>
<td>
20
</td>
<td>
4.0
</td>
<td>
8
</td>
<td>
6
</td>
<td>
1.25
</td>
</tr>
<tr>
<td>
8
</td>
<td>
680
</td>
<td>
15
</td>
<td>
6.491853096329675
</td>
<td>
8
</td>
<td>
0
</td>
<td>
1.25
</td>
</tr>
<tr>
<td>
9
</td>
<td>
120
</td>
<td>
60
</td>
<td>
2.0
</td>
<td>
13
</td>
<td>
0
</td>
<td>
0.7692307692307693
</td>
</tr>
</table>
Resulting Regression Line: y = 0.6563885197434527 + 0.1050388277621675 * x
<br/>
Index of Performance: 3.4915736059091667
<br/>
Index of Performance ignoring a: 9.520289033158615
<br/>
<h3>Experiment with 'Touchpad' aus input device
</h3><table border='1'>
<tr style='background-color:#AEAEAE'>
<td>
Trial
</td>
<td>
D
</td>
<td>
W
</td>
<td>
ID
</td>
<td>
Hits
</td>
<td>
Missed
</td>
<td>
MT
</td>
</tr>
<tr>
<td>
1
</td>
<td>
700
</td>
<td>
50
</td>
<td>
4.807354922057604
</td>
<td>
9
</td>
<td>
1
</td>
<td>
1.1111111111111112
</td>
</tr>
<tr>
<td>
2
</td>
<td>
30
</td>
<td>
10
</td>
<td>
2.584962500721156
</td>
<td>
8
</td>
<td>
0
</td>
<td>
1.25
</td>
</tr>
<tr>
<td>
3
</td>
<td>
280
</td>
<td>
25
</td>
<td>
4.459431618637297
</td>
<td>
10
</td>
<td>
0
</td>
<td>
1.0
</td>
</tr>
<tr>
<td>
4
</td>
<td>
20
</td>
<td>
20
</td>
<td>
1.0
</td>
<td>
12
</td>
<td>
0
</td>
<td>
0.8333333333333334
</td>
</tr>
<tr>
<td>
5
</td>
<td>
500
</td>
<td>
40
</td>
<td>
4.643856189774724
</td>
<td>
9
</td>
<td>
1
</td>
<td>
1.1111111111111112
</td>
</tr>
<tr>
<td>
6
</td>
<td>
300
</td>
<td>
100
</td>
<td>
2.584962500721156
</td>
<td>
10
</td>
<td>
0
</td>
<td>
1.0
</td>
</tr>
<tr>
<td>
7
</td>
<td>
160
</td>
<td>
20
</td>
<td>
4.0
</td>
<td>
9
</td>
<td>
0
</td>
<td>
1.1111111111111112
</td>
</tr>
<tr>
<td>
8
</td>
<td>
680
</td>
<td>
15
</td>
<td>
6.491853096329675
</td>
<td>
6
</td>
<td>
0
</td>
<td>
1.6666666666666667
</td>
</tr>
<tr>
<td>
9
</td>
<td>
120
</td>
<td>
60
</td>
<td>
2.0
</td>
<td>
13
</td>
<td>
0
</td>
<td>
0.7692307692307693
</td>
</tr>
</table>
Resulting Regression Line: y = 0.6647037012431731 + 0.11881925546104631 * x
<br/>
Index of Performance: 3.3059841569327846
<br/>
Index of Performance ignoring a: 8.41614430354548
<br/>
<h3>Experiment with 'Trackpoint' aus input device
</h3><table border='1'>
<tr style='background-color:#AEAEAE'>
<td>
Trial
</td>
<td>
D
</td>
<td>
W
</td>
<td>
ID
</td>
<td>
Hits
</td>
<td>
Missed
</td>
<td>
MT
</td>
</tr>
<tr>
<td>
1
</td>
<td>
700
</td>
<td>
50
</td>
<td>
4.807354922057604
</td>
<td>
10
</td>
<td>
0
</td>
<td>
1.0
</td>
</tr>
<tr>
<td>
2
</td>
<td>
30
</td>
<td>
10
</td>
<td>
2.584962500721156
</td>
<td>
10
</td>
<td>
1
</td>
<td>
1.0
</td>
</tr>
<tr>
<td>
3
</td>
<td>
280
</td>
<td>
25
</td>
<td>
4.459431618637297
</td>
<td>
7
</td>
<td>
7
</td>
<td>
1.4285714285714286
</td>
</tr>
<tr>
<td>
4
</td>
<td>
20
</td>
<td>
20
</td>
<td>
1.0
</td>
<td>
11
</td>
<td>
0
</td>
<td>
0.9090909090909092
</td>
</tr>
<tr>
<td>
5
</td>
<td>
500
</td>
<td>
40
</td>
<td>
4.643856189774724
</td>
<td>
8
</td>
<td>
0
</td>
<td>
1.25
</td>
</tr>
<tr>
<td>
6
</td>
<td>
300
</td>
<td>
100
</td>
<td>
2.584962500721156
</td>
<td>
10
</td>
<td>
1
</td>
<td>
1.0
</td>
</tr>
<tr>
<td>
7
</td>
<td>
160
</td>
<td>
20
</td>
<td>
4.0
</td>
<td>
10
</td>
<td>
0
</td>
<td>
1.0
</td>
</tr>
<tr>
<td>
8
</td>
<td>
680
</td>
<td>
15
</td>
<td>
6.491853096329675
</td>
<td>
6
</td>
<td>
7
</td>
<td>
1.6666666666666667
</td>
</tr>
<tr>
<td>
9
</td>
<td>
120
</td>
<td>
60
</td>
<td>
2.0
</td>
<td>
9
</td>
<td>
1
</td>
<td>
1.1111111111111112
</td>
</tr>
</table>
Resulting Regression Line: y = 0.7474968424186195 + 0.11168861389996153 * x
<br/>
Index of Performance: 3.142405963035037
<br/>
Index of Performance ignoring a: 8.95346414537556
<br/>
